add ut and fix base inferencer

mmagic/apis/inferencers/base_mmagic_inferencer.py

@@ -130,10 +130,10 @@ def __call__(self, **kwargs) -> Union[Dict, List[Dict]]:
         Returns:
             Union[Dict, List[Dict]]: Results of inference pipeline.
         """
-        if 'extra_parameters' in kwargs.keys():
-            if 'infer_with_grad' in kwargs['extra_parameters'].keys():
-                if kwargs['extra_parameters']['infer_with_grad']:
-                    results = self.base_call(**kwargs)
+        if ('extra_parameters' in kwargs.keys()
+                and 'infer_with_grad' in kwargs['extra_parameters'].keys()
+                and kwargs['extra_parameters']['infer_with_grad']):
+            results = self.base_call(**kwargs)
         else:
             with torch.no_grad():
                 results = self.base_call(**kwargs)

mmagic/apis/inferencers/diffusers_pipeline_inferencer.py

@@ -14,16 +14,19 @@ class DiffusersPipelineInferencer(BaseMMagicInferencer):
     """inferencer that predicts with text2image models."""
 
     func_kwargs = dict(
-        preprocess=['text', 'negative_prompt'],
+        preprocess=[
+            'text', 'negative_prompt', 'num_inference_steps', 'height', 'width'
+        ],
         forward=[],
         visualize=['result_out_dir'],
         postprocess=[])
 
-    extra_parameters = dict(height=None, width=None)
-
     def preprocess(self,
                    text: InputsType,
-                   negative_prompt: InputsType = None) -> Dict:
+                   negative_prompt: InputsType = None,
+                   num_inference_steps: int = 20,
+                   height=None,
+                   width=None) -> Dict:
         """Process the inputs into a model-feedable format.
 
         Args:
@@ -35,9 +38,14 @@ def preprocess(self,
         """
         result = self.extra_parameters
         result['prompt'] = text
-
         if negative_prompt:
             result['negative_prompt'] = negative_prompt
+        if num_inference_steps:
+            result['num_inference_steps'] = num_inference_steps
+        if height:
+            result['height'] = height
+        if width:
+            result['width'] = width
 
         return result
 

mmagic/models/archs/wrapper.py

@@ -183,6 +183,18 @@ def to(
         torch_device: Optional[Union[str, torch.device]] = None,
         torch_dtype: Optional[torch.dtype] = None,
     ):
+        """Put wrapped module to device or convert it to torch_dtype. There are
+        two to() function. One is nn.module.to() and the other is
+        diffusers.pipeline.to(), if both args are passed,
+        diffusers.pipeline.to() is called.
+
+        Args:
+            torch_device: The device to put to.
+            torch_dtype: The type to convert to.
+
+        Returns:
+            self: the wrapped module itself.
+        """
         if torch_dtype is None:
             self.model.to(torch_device)
         else:

tests/test_apis/test_inferencers/test_diffusers_pipeline_inferencer.py

@@ -0,0 +1,40 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import platform
+from mmengine.utils import digit_version
+from mmengine.utils.dl_utils import TORCH_VERSION
+
+from mmagic.apis.inferencers.diffusers_pipeline_inferencer import \
+    DiffusersPipelineInferencer
+from mmagic.utils import register_all_modules
+
+register_all_modules()
+
+
+@pytest.mark.skipif(
+    'win' in platform.system().lower()
+    or digit_version(TORCH_VERSION) <= digit_version('1.8.1'),
+    reason='skip on windows due to limited RAM'
+    'and get_submodule requires torch >= 1.9.0')
+def test_diffusers_pipeline_inferencer():
+    cfg = dict(
+        model=dict(
+            type='DiffusionPipeline',
+            from_pretrained='runwayml/stable-diffusion-v1-5'))
+
+    inferencer_instance = DiffusersPipelineInferencer(cfg, None)
+    text_prompts = 'Japanese anime style, girl'
+    negative_prompt = 'bad face, bad hands'
+    result = inferencer_instance(
+        text=text_prompts,
+        negative_prompt=negative_prompt,
+        height=128,
+        width=128)
+    assert result[1][0].size == (128, 128)
+
+
+def teardown_module():
+    import gc
+    gc.collect()
+    globals().clear()
+    locals().clear()